• Journal of Korean Society of Archives and Records Management
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• v.11 no.2
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• pp.33-55
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• 2011

The purpose of this study is to suggest EAD mandatory elements to share finding aids among the Archival Institutions of Korea. Three foreign EAD best practices for case studies: the EAD Best Practice at the Library of Congress(2008), the RLG Best Practice Guidelines for Encoded Archival Description(2002), and the OAC Best Practice Guidelines for Encoded Archival Description (2005) were analyzed. In addition, the Archival Description Rules(2008) as the data content standard of Korea and the descriptive elements which are were being used at Archival Institutions of Korea were analyzed. Based upon the results of comparisons among EAD elements, in-depth interviews were performed to investigate which EAD elements should be included in finding aids as a matter of exchange format. Based upon the literature review, case studies, and interviews, the rules and EAD elements for the archival institutions in Korea were designed and suggested.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.691-700
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• 2009

An event means a flow which has a time attribute such as the a symptom of patients, an interval event has the time period between the start-time-point and the end-time-point. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from interval event such as patient histories and purchase histories. In this paper, we suggest a method of temporal data mining that finds association rules of event causal relationships and predicts an occurrence of effect event based on discovered rules. Our method can predict the occurrence of an event by summarizing an interval event using the time attribute of an event and finding the causal relationship of event. As a result of simulation, this method can discover better knowledge than others by considering a lot of supports of an event and finding the significant rare relation on interval events which means an essential cause of an event, regardless of an occurrence support of an event in comparison with conventional data mining techniques.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.187-192
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• 2019

Majority problem and synchronization problem on cellular automata(CA) are hard to solve, since they are global problems while CA operate on local information. This paper proposes a way to find state transition rules of these problems. The rules of CA are represented as CMR(conditionally matching rules) and evolutionary algorithms are applied to find rules. We find many solution rules to these problems, compared the results with the previous studies, and demonstrated the effectiveness of CMR on one-dimensional cellular automata.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• Journal of the Chungcheong Mathematical Society
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• v.21 no.1
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• pp.117-127
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• 2008

The performance of a modified Armijo line search rule related to BFGS gradient type method with the results from other well-known line search rules are compared as well as analyzed. Although the modified Armijo rule does require as much computational cost as the other rules, it shows more efficient in finding local minima of unconstrained optimization problems. The sensitivity of the parameters used in the line search rules is also analyzed. The results obtained by implementing algorithms in Matlab for the test problems in [3] are presented.

• Journal of the Korea Convergence Society
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• v.3 no.2
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• pp.19-26
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• 2012

In accordance with the advent of smartphone and the growth of the Mobile App market, the Mobile game industry is being reorganized. So, This study is to be know the association rules between mobile game apps and mobile apps. Accordingly, To promote the Mobile Game App based on advertisement effectiveness that can be obtained from the characteristics of the game by finding out what to investigate.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.87-97
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• 2019

Data mining technology is applied to various fields because it is a technique for analyzing vast amount of data and finding useful information. In this paper, we propose a big data analysis method that uses Apriori algorithm, which is a data mining technique, to find the related factors that have negative and positive influences on school adjustment. Among Korea Child and Youth Panel Survey(KCYPS), data related to adjustment to school life and data showing parental inclinations were extracted from the data of fourth grade elementary school students, first year middle school students, and high school freshman students, respectively and we have mapped the useful association rules among them. As a result, the factors affecting school adjustment were different according to the timing of the growth process, we were able to find interesting rules by looking for connections between rules. On the other hand, the factors that positively influenced school adjustment were not significantly different from each other, and overall, they were associated with positive variables.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.293-298
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• 2006

Many data are stored in database. For getting any information from many data, we use the query sentences. These information is basic and simple. Data mining method is various. In this paper, we manage clustering and association rules. We present a method for finding the better association rules, and we solve a problem of the existing association rules. We propose and apply a new clustering method to fit for association rules. It is not clustering of the existing distance basis or category basis. If we find association rules of each clusters, we can get not only existing rules found in all transaction but also rules that will be characteristics of clusters. Through this study, we can expect that we will reduce the number of many transaction access in large databases and find association of small group.

• Journal of the Korean Society for information Management
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• v.22 no.4 s.58
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• pp.129-151
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• 2005

The advent of new technologies and descriptive tools, including the Web, XML, and EAD, have highlighted the necessity of content standard which can integrate and manage to library materials and archives and accommodate various finding aids for information exchange. This research analyzes both APPM2 and DACS. The former is an old established description rule. On the other hand, the latter keeps step with ISAD(G) and ISAAR(CPF) as adopting the international trend of archives and is able to describe the data structure of a variety of finding aids including MARC, EAD, etc. As a result, it presents the points to take into consideration as making descriptive rules about our archives.

• The Mathematical Education
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• v.62 no.2
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• pp.175-194
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• 2023

Despite the importance of pattern learning for elementary school students, few studies have investigated in detail the understanding of patterns of lower-grade students. This study aimed to analyze the understanding of patterns of second-grade elementary school students. Since the patterns in the second grade are taught through the unit called Finding Rules, students' understanding of patterns was compared and contrasted before and after they learned the unit. To this end, a written instrument to measure students' understanding of patterns was developed on the basis of previous studies on pattern learning for lower-grade students. A total of 189 students were analyzed. As a result of the study, the overall correct answer rates in the post-test were higher in most items than those in the pre-test, illustrating the positive effect of the specific unit. However, students found it difficult to find rules in which two components would change simultaneously either in geometric or numeric patterns, find patterns that would be similar in structure, represent geometric patterns into numeric patterns, find empty terms in increasing patterns, and reason the specific terms in patterns that can be differently interpreted. Based on these research results, this study sheds light on students' understanding of patterns and suggests implications to improve their understanding.